Proper related posts plugin for Jekyll - uses document correlation matrix on TF-IDF (optionally with Latent Semantic Indexing). Each document is tokenized and stemmed, every word found is treated as keyword for analysis (except for some stop words). TF-IDF matrix for the whole site is calculated (including extra provided weights), then if given accuraccy is lower than 1.0, LSI algorithm is used to compute new simplified vector space. Document correlation matrix is created using dot product of the matrix and its transpose. For each of the post' related documents are inserted into priority queue (sorted by score from document correlation matrix), assuming the score is greater than minimal required score. Selected few bests related posts are retrieven from the queue. Liquid template for each post is rendered and <related-posts /> is replaced with the outcomes of algorithm.

An implementation of the Lexrank Algorithm, which summarize corpus of text documents.

This gem removes punctuation and digits(optional), filters stopwords for the chosen language ('tr', 'en' or 'fr'), does stemming on the words and outputs an array of words with their frequencies.

Builds the LunrJS index and data documents during site generation

Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.

It's a text preprocessor that generate a libsvm input file

Jekyll plugin to show related posts based on the content, tags, and categories. The similarity is calculated using TF-IDF(term frequency-inverted document frequency). Since tags and categories are use-defined values, those are considered with higher weights than a content while calculating.